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141 Hours Free Data! Join the Speech Challenge Right Now!
2019.09.20

Introduction of OLR 2019

 

As an international event, the Oriental Language Recognition (OLR) Challenge series organized by Speechocean and Tsinghua University aims at boosting language recognition technology for oriental languages. Following the success of challenges held in last three years, OLR 2019 follows the same theme but will be more challenging and more interesting.

 

141 hours of speech recognition corpus covering 16 languages are totally free for each participant. Come and join this challenge right now!

 

Data Details


LanguagesHoursUsage
Mandarin / Cantonese / Japanese / Korean / Indonesian / Vietnamese / Russian89

Training Set (71 Hours)

Test Set (18 Hours)
Catalan / Greek / Telugu9Test Set
Sichuanese / Shanghainese / Hokkien3Development Set
Kazak / Tibetan / Uyghur35Training Set

Languages for Cross-channel:

Mandarin / Japanese / Vietnamese / Russian / Tibetan / Uyghur
5Test Set


Test Tasks

 

Task 1: Short-utterance LID, where the test utterances are as short as 1 second.

Task 2: Cross-channel LID, where test data is in different channels from the training set.

Task 3: Zero-resource LID, where no resources are provided for training before inference, but several reference utterances are provided for each language.

 

Important Dates

                           

July   16thRegistration   opened; training and development data released
October   1stRegistration   close
October   20thTest   data release
November   1stTest   result submission deadline
November   18thResults   announcement


Organization Committees

 

Tsinghua University

Speechocean

Xiamen University

Duke-Kunshan University

Northwestern Polytechnical University

 

 

Registration Procedure

 

Please send email to the olr19@cslt.org with the following information:

-- Team name

-- Institute

-- Participants

-- Duty person

-- Homepage of person / organization / company (If homepage is not available, any of your online papers in speech field are feasible.

 


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